• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.969-973
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• 2005

This paper presents a survey of software reliability modeling and it's application to pre-built software system combined with hardware such as numerical controller based on personal computer systems. Many a systems in these days are much more becoming software intensive and many software intensive systems are safety critical. For this reason, the technique well developed to measure of software reliability is very important for whom to assess such a system. This paper provides a brief idea of method to evaluate such a system's reliability based on hardware performance.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.467-474
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• 2013

It is common to measure multiple coverage metrics during software testing. Software reliability growth models and coverage growth functions have been applied to each coverage metric to evaluate software reliability; however, analysis results for the individual coverage metrics may conflict with each other. This paper proposes the virtual coverage metric of a normalized first principal component in order to avoid conflicting cases. The use of the virtual coverage metric causes a negligible loss of information.

• 한국게임학회 논문지
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• 제11권6호
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• pp.53-60
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• 2011

게임 서비스를 위한 소프트웨어의 경우 그 신뢰도에 대한 중요성은 지속적으로 증가하고 있다. 따라서 소프트웨어 신뢰도에 대한 평가 또한 매우 중요하다. 본 연구에서는 개발 공정에 대한 대표 활동인 확인 및 검증이 소프트웨어 신뢰도에 어떠한 영향을 미치는가를 정량적으로 분석하기 위하여 실험을 수행하였다. 이러한 실험 결과는, BBN (Bayesian Belief Network) 기반 신뢰도 평가와 같이, 개발 공정에 근거하여 신뢰도를 평가할 때 매우 유용한 근거 자료로 활용될 것이다.

• Journal of Information Technology Applications and Management
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• 제12권2호
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• pp.1-13
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• 2005

This paper proposes a software-reliability growth model incoporating the amount of testing effort expended during the software testing phase after developing it. The time-dependent behavior of testing effort expenditures is described by a Logistic curve. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, This paper discusses the relations between testing time and reliability and between duration following failure fixing and reliability are studied. SRGM in several literatures has used the exponential curve, Railleigh curve or Weibull curve as an amount of testing effort during software testing phase. However, it might not be appropriate to represent the consumption curve for testing effort by one of already proposed curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be adequately expressed as a software development/testing effort curve and that it gives a good predictive capability based on real failure data.

• 전기전자학회논문지
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• 제19권3호
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• pp.431-439
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• 2015

최근 무기체계에서 소프트웨어가 담당하는 기능이 많아지고, 복잡해지면서 소프트웨어의 품질의 중요성에 대해서 대두가 되고 있다. 소프트웨어의 품질요소는 기능성, 사용성, 유지보수성, 신뢰성이 있으며, 이중 최근 들어 중요시되는 항목이 신뢰성 분야이다. 신뢰성 시험은 분석을 통한 정적 시험과 수행을 통한 동적 시험방법이 있으며, 실수행을 통해 검증하는 동적시험은 정적시험에 비해 많은 기간이 필요하며 복잡성이 높아질 경우 더욱 증가되는 경향을 보인다. 본 논문에서는 다기능 레이더 소프트웨어 개발시 수행한 신뢰성시험 절차 및 결과를 기술하고 이를 통해 효과적으로 시험기간을 줄일 수 있는 신뢰성시험 방법을 제안한다.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2002년도 정기학술대회
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• pp.273-273
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• 2002

In this paper, we investigate a software release policy with software reliability growth factor during the warranty period by assuming that the software reliability growth is assumed to occur after the testing phase with probability p and the software reliability growth is not assumed to occur after the testing phase with probability 1-p. The optimal release policy to minimize the expected total software cost is discussed. Numerical examples are shown to illustrate the results of the optimal policy. And we consider a Bayesian decision theoretic approach to determine an optimal software release policy. This approach enables us to update our uncertainty when determining optimal software release time, When the failure time is Weibull distribution with uncertain parameters, a bayesian approach is established. Finally, numerical examples are presented for illustrative propose.

• 한국정보처리학회논문지
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• 제6권3호
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• pp.615-621
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• 1999

On the aspect of on-time and development resource use, it is very important to predict the software release time during the software development process. In this paper, we present the optimal release problem based on the evaluation indicator and cost evaluation. And also we show the optimal release point considered with both of them. We applied the Exponential Software Reliability Growth Model(E-SRGM) and Testing-effort dependent Software Reliability Growth Model(Te-SRGM) and decided the software release time according to software reliability indicator. As a result of two models comparison, we verify the Te-SRGM is more adopted in our switching system software.

• 한국정보처리학회논문지
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• 제3권2호
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• pp.231-241
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• 1996

지금까지 많은 소프트웨어 신뢰도 성장 모델들의 제안되어 왔으나 현상을 완전히 고려한 소프트웨어 신뢰도 모델은 아직 개발되어 있지 못한 실정이다. 따라서 다양한 소프트웨어 신뢰도 모델이 계속 개발되어 모든 현상을 고려할 수 있는 모델의 개발이 필요하다고 볼 수 있다. 이와 같은 목적하에 본 논문에서는 발견되는 오류를 오류의 특성에 따라 3종류로 분류하였고, 또한 각 오류의 분류된 오류의 발견 시점 등을 고려 하여 소프트웨어의 신뢰도를 측정할 수 있는 신뢰도 모델을 제안하였다. 그리고모델의 파라메타의 추정 및 신뢰성 평가를 위해실측 데이타를 이용하여 고찰하였으며, 본 논문의 적합성을 검증하기 위해 기존의 모델과 비교 분석 하였다.

• 한국IT서비스학회지
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• 제8권4호
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• 한국신뢰성학회지:신뢰성응용연구
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• 제1권2호
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• pp.175-181
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• 2001

Recently, the quality of software has become a major issue. The statistical models used in making predictions about the quality of software are termed software reliability growth models (SRGM). However, the existing SRGMs have not been satisfactory in predicting software reliability behavior (Keiller and Miller(1991), Keiller and Littlewood(1984), Musa(1987)). In this paper, we present a fuzzy-based interval estimation of software errors (failures).